paraFaceTest: an ensemble of regression tree-based facial features extraction for efficient facial paralysis classification

• Published in: BMC medical imaging Vol. 19; no. 1; pp. 30 - 14
• Main Authors: Barbosa, Jocelyn, Seo, Woo-Keun, Kang, Jaewoo
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.04.2019; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Asymmetry; Boundaries; Classification; Classifiers; Computer science; Data mining; Ensemble of regression trees; Evaluation; Face; Face recognition; Facial paralysis; Facial Paralysis - classification; Facial Paralysis - psychology; Facial paralysis evaluation framework; Facial paralysis objective evaluation; Feature extraction; Human motion; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; International conferences; Introversion, Psychological; Iris detection; Medical treatment; Methods; Muscles; Novels; Paralysis; Polynomials; Regression Analysis; Salient point detection; Sensitivity and Specificity; Social aspects; Social factors; Social interactions; Stress (Psychology); Technical Advance; New York; United States > US; Facial paralysis classification; Facial paralysis evaluation framework; Salient point detection; Facial paralysis objective evaluation; Ensemble of regression trees; Iris detection

Facial paralysis (FP) is a neuromotor dysfunction that losses voluntary muscles movement in one side of the human face. As the face is the basic means of social interactions and emotional expressions among humans, individuals afflicted can often be introverted and may develop psychological distress,...